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Modeling of Gap Gene Expression in Drosophila Kruppel Mutants

Overview of attention for article published in PLoS Computational Biology, August 2012
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Title
Modeling of Gap Gene Expression in Drosophila Kruppel Mutants
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002635
Pubmed ID
Authors

Konstantin Kozlov, Svetlana Surkova, Ekaterina Myasnikova, John Reinitz, Maria Samsonova

Abstract

The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr) on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations.

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Geographical breakdown

Country Count As %
United States 3 6%
France 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Researcher 13 25%
Student > Bachelor 6 11%
Student > Master 5 9%
Student > Doctoral Student 4 8%
Other 7 13%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 42%
Biochemistry, Genetics and Molecular Biology 10 19%
Physics and Astronomy 6 11%
Engineering 2 4%
Computer Science 2 4%
Other 3 6%
Unknown 8 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2012.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#8,208
of 8,960 outputs
Outputs of similar age
#146,676
of 186,645 outputs
Outputs of similar age from PLoS Computational Biology
#91
of 103 outputs
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